Modeling correlated main‐chain motions in proteins for flexible molecular recognition

Abstract

We describe a new method for modeling protein and ligand main-chain flexibility, and show its ability to model flexible molecular recognition. The goal is to sample the full conformational space, including large-scale motions that typically cannot be reached in molecular dynamics simulations due to the computational intensity, as well as conformations that have not been observed yet by crystallography or NMR. A secondary goal is to assess the degree of flexibility consistent with protein-ligand recognition. Flexibility analysis of the target protein is performed using the graph-theoretic algorithm FIRST, which also identifies coupled networks of covalent and noncovalent bonds within the protein. The available conformations of the flexible regions are then explored with ROCK by random-walk sampling of the rotatable bonds. ROCK explores correlated motions by only sampling dihedral angles that preserve the coupled bond networks in the protein and generates conformers with good stereochemistry, without using a computationally expensive potential function. A representative set of the conformational ensemble generated this way can be used as targets for docking with SLIDE, which handles the flexibility of protein and ligand side-chains. The realism of this protein main-chain conformational sampling is assessed by comparison with time-resolved NMR studies of cyclophilin A motions. ROCK is also effective for modeling the flexibility of large cyclic and polycyclic ligands, as demonstrated for cyclosporin and zearalenol. The use of this combined approach to perform docking with main-chain flexibility is illustrated for the cyclophilin A-cyclosporin complex and the estrogen receptor in complex with zearalenol, while addressing the question of how much flexibility is allowed without hindering molecular recognition.

An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

“…We also compared the rigid clusters formed by FIRST for barnase from previous work [28,27,36] and found that the largest rigid cluster identified by FIRST consisting of α1 helix and β1-β5 indeed exhibited overall smaller distance fluctuations in DTA. We also note that comparing our results with that of FIRST for cyclophilin A [47], we found good agreement in terms of the location of flexible versus constrained regions in the enzyme.…”

Hierarchical Docking of Databases of Multiple Ligand Conformations

“…This was the problem encountered in neutral endopeptidase; as alluded to, the problem can be partly addressed by generating multiple ensembles based on multiple different rigid fragments, [34] or by altering the location of the docking spheres and possibly performing multiple docking calculations. Finally, this method had not been investigated heavily in the context of allowing for receptor flexibility, as other methods have done [11,13,[36][37][38][39][40] (though see ref. [41]).…”

StoneHinge: Hinge prediction by network analysis of individual protein structures

“…Ligands can then be independently docked into each protein conformation using SLIDE, 43 as was demonstrated for fully flexible docking of the cyclic peptide cyclosporin with its receptor, cyclophilin A. 44 StoneHingeP already provides the input files needed by ROCK, making the process easy to automate.…”